Öz
Agent-Based Computational Economics (from now ACE) is a dynamic field that combines computational techniques with economic theory to model and analyze complex adaptive systems. It originated from Guy Orcutt's pioneering work in 1957, which introduced microsimulation for economic transactions and interactions. ACE has evolved significantly, particularly with advanced computational technologies in the mid-1990s, leading to the rise of agent-based models (ABMs) and complex adaptive systems (CAS). These advancements allow researchers to simulate individual agents' behaviors and interactions within an economy, revealing emergent properties of economic systems. ACE sets itself apart from classical economic theory by incorporating the concept of bounded rationality, which acknowledges that decision makers have limited information and cognitive capabilities. This approach also emphasizes the constant interaction among these decision makers as well as the existence of multiple equilibrium situations. Overall, it offers a heterodox perspective that diverges from the traditional economic modeling methods. However, ACE studies face some challenges and limits. The main objective of this research is to conduct a literature review, analyze the historical progression of ACE, recent advancements and challenges and explore the potential future trajectory of this economic approach.
Anahtar Kelimeler
Etmen Tabanlı Kompütasyonel İktisat Sınırlı Rasyonellik Heteredoks İktisat Çoklu Denge Kompütasyonel Teknoloji
Kaynakça
- Anderson, P., Arrow, K. and Pines, D., (1988). The economy is an evolving complex system. SFI Studies in the Sciences of Complexity. Addison-Wesley Longman, Redwood City, CA.
- Auyang, S.Y. (2004). Synthetic analysis of Complex Systems 1 - Theories. Paper presented at University of Sydney (History and Philosophy of Science). http://www.usyd.edu.au/su/hps/newevents/Auyang1.html.
- Ballot, G. (2002). Modeling the labor market as an evolving institution: model ARTEMİS. Journal of Economic Behavior and Organization, 49, 51–77.
- Baroni, E., & Richiardi, M. (2007). Orcutt’s Vision, 50 years on. Laboratorio Riccardo Revelli Centre For Employment Studies, Working Paper no. 65, https://faculty.sites.iastate.edu/tesfatsi/archive/tesfatsi/OrcuttVision50YearsOn.BaroniRichiardi.2007.pdf
- Bennett, R. and Bergmann, B. (1986). Microsimulated Transactions Model of the United States Economy. Baltimore: Johns Hopkins University Press.
- Bergmann, B. R. (1974). A microsimulation of the macroeconomy with explicitly represented money flows. Annals of Economic and Social Measurement, 3(3), 475–489.
- Borshchev, A. and Filippov, A. (2004). From System Dynamics and Discrete Event to Practical Agent-Based Modeling: Reasons, Techniques, and Tools. Proceedings, 22nd International Conference of the System Dynamics Society, Oxford, England, July 25-29.
- Bruun, C. (2004). Agent-Based Computational Economics – An Introduction. https://www.researchgate.net/publication/228729397_Agent-based_computational_economics-An_introduction
- Cincotti, Silvano, R. M. and Teglio, A. (2010). Credit money and macroeconomic instability in the agent-based model and simulator Eurace. Economics: The Open- Access, Open-Assessment E-Journal, 4(1), 20100026.
- Chen, S.H., Jain, L. and Tai, C.C. (2006). Computational Economics: A Perspective from Computational Intelligence. Idea Group Inc., 318.
- Chen, S.H., & Yeh, C. H. (2001). Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control 25(3-4), 363-393.
- Cohen, K. J. (1960). Simulation of the firm. The American Economic Review, 50(2), 534-540.
- Dawid, H., Gemkow, S., Harting, P., and Neugart, M. (2012). Labor market integration policies and the convergence of regions: the role of skills and technology diffusion. Journal of Evolutionary Economics, 22(3), 543–562.
- Dawid, H. and Neugart, M. (2011). Agent-based models for economic policy design. Eastern Economic Journal, 37(1), 44–50.
- Deissenberg, C., van der Hoog, S. and Dawid, H. (2008). EURACE: A massively parallel agent-based model of the European economy. Applied Mathematics and Computation, 204, 541–552.
- Dilaver, Ö., Jump, R. C., & Levine, P. (2018). Agent-based macroeconomics and dynamic stochastic general equilibrium models: Where do we go from here?. Journal of Economic Surveys, 32(4), 1134-1159.
- Eliasson, G. (1977). Competition and market processes in a simulation model of the Swedish economy. The American Economic Review, 67, 277–281.
- Eliasson, G. (1988). MOSES (Model of the Swedish Economic System): A Presentation of the Swedish Micro-to-Macro Econometric Model, Working Paper Series no.197, Research Institute of Industrial Economics, https://EconPapers.repec.org/RePEc:hhs:iuiwop:0197.
- Eliasson, G. (2018). Why Complex, Data Demanding and Difficult to Estimate Agent Based Models? Lessons from a Decades Long Research Program. International Journal of Microsimulation, 11(1), 4-60.
- Gilbert, N. (2008). Agent-Based Models, Quantitative Applications in the Social Sciences, 153, SAGE Publications.
- Gleiser, I., Farmer, D., Mojsa, J. and Bydlon, S. (2024). How agent-based models powered by HPC are enabling large scale economic simulations. AWS HPC Blog. https://aws.amazon.com/tr/blogs/hpc/how-agent-based-models-powered-by-hpc-are-enabling-large-scale-economic-simulations/
- Grazzini, J., Richiardi, M. and Sella, L. (2012). Small sample bias in MSM estimation of agent-based models. In Andrea Teglio, Simone Alfarano, E. C.-C. M. G.-V., editor, Managing Market Complexity. The Approach of Artificial Economics., Lecture Notes in Economics and Mathematical Systems. Springer.
- Grazzini, J. and Richiardi, M. (2013). Consistent estimation of agent-based models by simulated minimum distance. Working Paper 130/2013, LABORatorio R. Revelli.
- Hailegiorgis, A., Crooks, A. and Cioffi-Revilla, C. (2018). An agent-based model of rural households’ adaptation to climate change. Journal of Artificial Societies and Social Simulation, 21(4).
- Kendrick, A.D., Mercado, P.R. and Amman, H.M. (2006). Computational Economics. Princeton University Press.
- Kirman, A. (1992). Whom or what does the representative agent represent?.Journal of Economic Perspectives. 6(2), 117-36.
- Lehtinen, A. and Kuorikoski, J. (2007). Computing the perfect model: Why do economists shun simulation? Philosophy of Science, 74, 304-329.
- Markose, S. (2004). Novelty in complex adaptive systems (CAS) dynamics: a computational theory of actor innovation. Physica A: Statistical Mechanics and its Applications, 344(1), 41-49.
- Markose, S. (2005). Computability and evolutionary complexity: markets as complex adaptive systems (CAS). Economic Journal, 115(504), F159-F192.
- Meyer, R. N. (2023). Intrinsic unrealism: The ıneffectiveness of neoclassical economic models. The Gettysbyrg Economic Review, 12(6), 101-115.
- Orcutt, G. (1957). A new type of socio-economics system. Review of Economics and Statistics, 39, 116-123.
- Orcutt, G., Greenberger, M., Korbel, J. and Rivlin, A. M. (1961). Microanalysis of Socioeconomic Systems: A Simulation Study. New York: Harpers.
- Richiardi, M. (2014). The Missing Link: AB Models and Dynamic Microsimulation. In: Leitner, S., Wall, F. (Eds). Artificial Economics and Self Organization. Lecture Notes in Economics and Mathematical Systems, Springer.
- Russo, A., Tedeschi, G. and Fagiolo, G. (2018). Interaction. In D. Delli Gatti, G. Fagiolo, M. Gallegati, M. Richiardi and A. Russo (Eds.), Agent-Based Models in Economics: A Toolkit (pp. 109–142). Cambridge: Cambridge University Press.
- Schinckus, C. (2019). Agent-based modelling and economic complexity: A diversified perspective. Journal of Asian Business and Economic Studies, 26 (2), 170-188. https://doi.org/10.1108/JABES-12-2018-0108
- Seri, R., Secchi, D., Martinoli, M. (2022). Randomness, emergence and causation: A historical perspective of simulation in the social sciences. In: Albeverio, S., Mastrogiacomo, E., Rosazza Gianin, E., Ugolini, S. (Eds). Complexity and Emergence. CEIM 2018. Springer Proceedings in Mathematics & Statistics, vol 383. Springer, Cham. https://doi.org/10.1007/978-3-030-95703-2_7
- Sitthiyot, T. (2015). Macroeconomic and financial management in an uncertain world: What can we learn from complexity science? Thailand and The World Economy, 33(3), 1-23.
- Suresh, S. G. (2023). Rational Economic Models of Homo Sapiens. Social Science Research Network (SSRN:ID4235655).
- Tefsatsion, L. (2006). Agent-based computational economics: A Constructive approach to economic theory. In Handbook of Computational Economics (pp 831-880), Elsevier.
- Tesfatsion, L. (2021). Agent-based computational economics: Overview and brief history. In Artificial intelligence, learning and computation in economics and finance, 41-58.
- Tefsation, L. (2024). Agent-based computational economics (ACE) and related topics. https://faculty.sites.iastate.edu/tesfatsi/archive/tesfatsi/aintro.htm
Öz
Agent-Based Computational Economics (from now ACE) is a dynamic field that combines computational techniques with economic theory to model and analyze complex adaptive systems. It originated from Guy Orcutt's pioneering work in 1957, which introduced microsimulation for economic transactions and interactions. ACE has evolved significantly, particularly with advanced computational technologies in the mid-1990s, leading to the rise of agent-based models (ABMs) and complex adaptive systems (CAS). These advancements allow researchers to simulate individual agents' behaviors and interactions within an economy, revealing emergent properties of economic systems. ACE sets itself apart from classical economic theory by incorporating the concept of bounded rationality, which acknowledges that decision-makers have limited information and cognitive capabilities. This approach also emphasizes the constant interaction among these decision-makers and the existence of multiple equilibrium situations. Overall, it offers a heterodox perspective that diverges from the traditional economic modeling methods. However, ACE studies face some challenges and limits. The main objective of this research is to conduct a literature review, analyze the historical progression of ACE, examine recent advancements and challenges, and explore the potential future trajectory of this economic approach.
Anahtar Kelimeler
Agent Based Computational Economics Bounded Rationality Heterodox Economics Multiple Equilibrium Computational Technology
Kaynakça
- Anderson, P., Arrow, K. and Pines, D., (1988). The economy is an evolving complex system. SFI Studies in the Sciences of Complexity. Addison-Wesley Longman, Redwood City, CA.
- Auyang, S.Y. (2004). Synthetic analysis of Complex Systems 1 - Theories. Paper presented at University of Sydney (History and Philosophy of Science). http://www.usyd.edu.au/su/hps/newevents/Auyang1.html.
- Ballot, G. (2002). Modeling the labor market as an evolving institution: model ARTEMİS. Journal of Economic Behavior and Organization, 49, 51–77.
- Baroni, E., & Richiardi, M. (2007). Orcutt’s Vision, 50 years on. Laboratorio Riccardo Revelli Centre For Employment Studies, Working Paper no. 65, https://faculty.sites.iastate.edu/tesfatsi/archive/tesfatsi/OrcuttVision50YearsOn.BaroniRichiardi.2007.pdf
- Bennett, R. and Bergmann, B. (1986). Microsimulated Transactions Model of the United States Economy. Baltimore: Johns Hopkins University Press.
- Bergmann, B. R. (1974). A microsimulation of the macroeconomy with explicitly represented money flows. Annals of Economic and Social Measurement, 3(3), 475–489.
- Borshchev, A. and Filippov, A. (2004). From System Dynamics and Discrete Event to Practical Agent-Based Modeling: Reasons, Techniques, and Tools. Proceedings, 22nd International Conference of the System Dynamics Society, Oxford, England, July 25-29.
- Bruun, C. (2004). Agent-Based Computational Economics – An Introduction. https://www.researchgate.net/publication/228729397_Agent-based_computational_economics-An_introduction
- Cincotti, Silvano, R. M. and Teglio, A. (2010). Credit money and macroeconomic instability in the agent-based model and simulator Eurace. Economics: The Open- Access, Open-Assessment E-Journal, 4(1), 20100026.
- Chen, S.H., Jain, L. and Tai, C.C. (2006). Computational Economics: A Perspective from Computational Intelligence. Idea Group Inc., 318.
- Chen, S.H., & Yeh, C. H. (2001). Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control 25(3-4), 363-393.
- Cohen, K. J. (1960). Simulation of the firm. The American Economic Review, 50(2), 534-540.
- Dawid, H., Gemkow, S., Harting, P., and Neugart, M. (2012). Labor market integration policies and the convergence of regions: the role of skills and technology diffusion. Journal of Evolutionary Economics, 22(3), 543–562.
- Dawid, H. and Neugart, M. (2011). Agent-based models for economic policy design. Eastern Economic Journal, 37(1), 44–50.
- Deissenberg, C., van der Hoog, S. and Dawid, H. (2008). EURACE: A massively parallel agent-based model of the European economy. Applied Mathematics and Computation, 204, 541–552.
- Dilaver, Ö., Jump, R. C., & Levine, P. (2018). Agent-based macroeconomics and dynamic stochastic general equilibrium models: Where do we go from here?. Journal of Economic Surveys, 32(4), 1134-1159.
- Eliasson, G. (1977). Competition and market processes in a simulation model of the Swedish economy. The American Economic Review, 67, 277–281.
- Eliasson, G. (1988). MOSES (Model of the Swedish Economic System): A Presentation of the Swedish Micro-to-Macro Econometric Model, Working Paper Series no.197, Research Institute of Industrial Economics, https://EconPapers.repec.org/RePEc:hhs:iuiwop:0197.
- Eliasson, G. (2018). Why Complex, Data Demanding and Difficult to Estimate Agent Based Models? Lessons from a Decades Long Research Program. International Journal of Microsimulation, 11(1), 4-60.
- Gilbert, N. (2008). Agent-Based Models, Quantitative Applications in the Social Sciences, 153, SAGE Publications.
- Gleiser, I., Farmer, D., Mojsa, J. and Bydlon, S. (2024). How agent-based models powered by HPC are enabling large scale economic simulations. AWS HPC Blog. https://aws.amazon.com/tr/blogs/hpc/how-agent-based-models-powered-by-hpc-are-enabling-large-scale-economic-simulations/
- Grazzini, J., Richiardi, M. and Sella, L. (2012). Small sample bias in MSM estimation of agent-based models. In Andrea Teglio, Simone Alfarano, E. C.-C. M. G.-V., editor, Managing Market Complexity. The Approach of Artificial Economics., Lecture Notes in Economics and Mathematical Systems. Springer.
- Grazzini, J. and Richiardi, M. (2013). Consistent estimation of agent-based models by simulated minimum distance. Working Paper 130/2013, LABORatorio R. Revelli.
- Hailegiorgis, A., Crooks, A. and Cioffi-Revilla, C. (2018). An agent-based model of rural households’ adaptation to climate change. Journal of Artificial Societies and Social Simulation, 21(4).
- Kendrick, A.D., Mercado, P.R. and Amman, H.M. (2006). Computational Economics. Princeton University Press.
- Kirman, A. (1992). Whom or what does the representative agent represent?.Journal of Economic Perspectives. 6(2), 117-36.
- Lehtinen, A. and Kuorikoski, J. (2007). Computing the perfect model: Why do economists shun simulation? Philosophy of Science, 74, 304-329.
- Markose, S. (2004). Novelty in complex adaptive systems (CAS) dynamics: a computational theory of actor innovation. Physica A: Statistical Mechanics and its Applications, 344(1), 41-49.
- Markose, S. (2005). Computability and evolutionary complexity: markets as complex adaptive systems (CAS). Economic Journal, 115(504), F159-F192.
- Meyer, R. N. (2023). Intrinsic unrealism: The ıneffectiveness of neoclassical economic models. The Gettysbyrg Economic Review, 12(6), 101-115.
- Orcutt, G. (1957). A new type of socio-economics system. Review of Economics and Statistics, 39, 116-123.
- Orcutt, G., Greenberger, M., Korbel, J. and Rivlin, A. M. (1961). Microanalysis of Socioeconomic Systems: A Simulation Study. New York: Harpers.
- Richiardi, M. (2014). The Missing Link: AB Models and Dynamic Microsimulation. In: Leitner, S., Wall, F. (Eds). Artificial Economics and Self Organization. Lecture Notes in Economics and Mathematical Systems, Springer.
- Russo, A., Tedeschi, G. and Fagiolo, G. (2018). Interaction. In D. Delli Gatti, G. Fagiolo, M. Gallegati, M. Richiardi and A. Russo (Eds.), Agent-Based Models in Economics: A Toolkit (pp. 109–142). Cambridge: Cambridge University Press.
- Schinckus, C. (2019). Agent-based modelling and economic complexity: A diversified perspective. Journal of Asian Business and Economic Studies, 26 (2), 170-188. https://doi.org/10.1108/JABES-12-2018-0108
- Seri, R., Secchi, D., Martinoli, M. (2022). Randomness, emergence and causation: A historical perspective of simulation in the social sciences. In: Albeverio, S., Mastrogiacomo, E., Rosazza Gianin, E., Ugolini, S. (Eds). Complexity and Emergence. CEIM 2018. Springer Proceedings in Mathematics & Statistics, vol 383. Springer, Cham. https://doi.org/10.1007/978-3-030-95703-2_7
- Sitthiyot, T. (2015). Macroeconomic and financial management in an uncertain world: What can we learn from complexity science? Thailand and The World Economy, 33(3), 1-23.
- Suresh, S. G. (2023). Rational Economic Models of Homo Sapiens. Social Science Research Network (SSRN:ID4235655).
- Tefsatsion, L. (2006). Agent-based computational economics: A Constructive approach to economic theory. In Handbook of Computational Economics (pp 831-880), Elsevier.
- Tesfatsion, L. (2021). Agent-based computational economics: Overview and brief history. In Artificial intelligence, learning and computation in economics and finance, 41-58.
- Tefsation, L. (2024). Agent-based computational economics (ACE) and related topics. https://faculty.sites.iastate.edu/tesfatsi/archive/tesfatsi/aintro.htm
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Heterodoks İktisat |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 23 Mayıs 2025 |
| Yayımlanma Tarihi | 4 Haziran 2025 |
| Gönderilme Tarihi | 26 Temmuz 2024 |
| Kabul Tarihi | 7 Aralık 2024 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 40 Sayı: 2 |
İzmir İktisat Dergisi
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https://kutuphane.deu.edu.tr/yayinevi/
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https://dergipark.org.tr/tr/pub/ije/contacts
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